Update main.py

main.py

@@ -504,6 +504,25 @@ async def get_sorted_scores(data: CoachingCodeRequest):
     merged_data = pd.merge(merged_journal_panic, test_df_processed, on='user_id', how='outer')
     merged_data_cleaned = merged_data.dropna(subset=['productivity_yes_no', 'productivity_rate', 'panic_button', 'test_chapter'], how='all')
 
+    def process_group(group):
+            # Panic button counts
+        panic_button_series = group['panic_button'].dropna()
+        panic_button_dict = panic_button_series.value_counts().to_dict()
+
+            # Test scores aggregation
+        test_scores = group[['test_chapter', 'test_score']].dropna()
+        test_scores['test_score'] = pd.to_numeric(test_scores['test_score'], errors='coerce')
+
+            # Create the test_scores_dict excluding NaN values
+        test_scores_dict = test_scores.groupby('test_chapter')['test_score'].mean().dropna().to_dict()
+
+        return pd.Series({
+            'productivity_yes_no': group['productivity_yes_no'].iloc[0],
+            'productivity_rate': group['productivity_rate'].iloc[0],
+            'panic_button': panic_button_dict,
+            'test_scores': test_scores_dict
+        })
+
     # Define scoring weights
     academic_weights = {'BACKLOGS': -5, 'MISSED CLASSES': -4, 'NOT UNDERSTANDING': -3, 'BAD MARKS': -3, 'LACK OF MOTIVATION': -3}
     non_academic_weights = {'EMOTIONAL FACTORS': -3, 'PROCRASTINATE': -2, 'LOST INTEREST': -4, 'LACK OF FOCUS': -2, 'GOALS NOT ACHIEVED': -2, 'LACK OF DISCIPLINE': -2}